NHS Digital Data Release Register - reformatted
South London And Maudsley NHS Foundation Trust projects
275 data files in total were disseminated unsafely (information about files used safely is missing for TRE/"system access" projects).
🚩 South London And Maudsley NHS Foundation Trust was sent multiple files from the same dataset, in the same month, both with optouts respected and with optouts ignored. South London And Maudsley NHS Foundation Trust may not have compared the two files, but the identifiers are consistent between datasets, and outside of a good TRE NHS Digital can not know what recipients actually do.
MR808 - SLaM IG Clinical Dataset Linking Service — DARS-NIC-292279-Z2S5T
Opt outs honoured: Y, N, Yes - patient objections upheld, No - data flow is not identifiable, Anonymised - ICO Code Compliant, Identifiable, Yes, No (Section 251, Section 251 NHS Act 2006)
Legal basis: Section 251 approval is in place for the flow of identifiable data, Approved researcher accreditation under section 39(4)(i) and 39(5) of the Statistical Registration Service Act 2007 , National Health Service Act 2006 - s251 - 'Control of patient information'. , Health and Social Care Act 2012 – s261(7), Health and Social Care Act 2012 s261(7); National Health Service Act 2006 - s251 - 'Control of patient information'., Health and Social Care Act 2012 s261(7); Other-Section 251, Health and Social Care Act 2012 s261(7); Other-Section 251 HRA approval, Health and Social Care Act 2012 s261(7)
Purposes: No (NHS Trust)
Sensitive: Sensitive, and Non Sensitive, and Non-Sensitive
When:DSA runs 2018-11-01 — 2021-09-30 2017.09 — 2022.12.
Access method: Ongoing, One-Off
Data-controller type: SOUTH LONDON AND MAUDSLEY NHS FOUNDATION TRUST
Sublicensing allowed: No
- MRIS - Cause of Death Report
- MRIS - Flagging Current Status Report
- Hospital Episode Statistics Admitted Patient Care
- Hospital Episode Statistics Outpatients
- Hospital Episode Statistics Accident and Emergency
- MRIS - Cohort Event Notification Report
- Hospital Episode Statistics Critical Care
- Civil Registration - Deaths
- HES-ID to MPS-ID HES Admitted Patient Care
- HES-ID to MPS-ID HES Outpatients
- Emergency Care Data Set (ECDS)
- Hospital Episode Statistics Accident and Emergency (HES A and E)
- Hospital Episode Statistics Admitted Patient Care (HES APC)
- Hospital Episode Statistics Critical Care (HES Critical Care)
- Hospital Episode Statistics Outpatients (HES OP)
- Civil Registrations of Death
The South London and Maudsley NHS Foundation (SLaM) Trust provides the widest range of NHS mental health services in the UK. It also includes the National Institute for Health Research (NIHR) Biomedical Research Centre and Dementia Unit which works in partnership with the Institute of Psychiatry, Psychology & Neuroscience at King’s College London. Together they aim to develop more individualised treatments and support advances in the prevention, diagnosis, treatment and care of mental ill health and dementia. To do this, they bring together researchers, clinicians, allied health professionals and service users from across the University/Trust partnership to work together better in order to meet the challenges of finding better treatments and improved care for patients.
They form part of the NIHR infrastructure and one of 11 NIHR Biomedical Research Centres in England. However, they are the only Biomedical Research Centre specialising in the full spectrum of mental health research across the age range from infancy through to older adults. They are one of four centres in the country specifically tasked with meeting the Government's challenge to develop new tests and treatments to improve outcomes for people with dementia.
The Mental Health Biomedical Research Centre and Dementia Unit (BRC/U) has developed the Clinical Record Interactive Search (CRIS) system to provide authorised researchers with regulated access to de-identified information extracted from the South London and Maudsley NHS Foundation Trust (SLaM) electronic clinical records system. CRIS helps researchers study real life situations in large quantities, looking for patterns and trends - e.g. what treatments work for some but do not work for others.
Since 2011, SLaM has periodically linked the cohort to HES and ONS data via the HSCIC. In addition, the HSCIC has supplied HES data for residents within SLaM’s geographic catchment (Lambeth, Southwark, Lewisham and Croydon boroughs) with an indicator variable for those residents with data also present on the SLaM Case Register (i.e. who have received SLaM services), and a pseudonymised identifier allowing linkage of HES and SLaM Case Register data. This data is used to enhance CRIS data so that it can be used for the purpose of identifying health inequalities for patients with mental disorders. The HES and ONS data are not added to CRIS. The data are stored separately in the SLaM Clinical Data Linkage Service (CDLS) and are only accessible to a restricted number of approved technical support staff.
The objective of the data collection is to create a research resource to be used for research projects aiming to investigate physical health outcomes (including mortality) and receipt of health care in people with mental disorders attending secondary mental health care services provided by SLaM.
Projects are subject to individual approval by the CRIS Oversight Committee. The CRIS Oversight Committee carries representation from the SLaM Caldicott Guardian and is chaired by a service user. The CRIS Oversight Committee is responsible for ensuring all research applications comply with ethical and legal guidelines. It closely reviews, monitors and audits applications to access CRIS and the analyses subsequently carried out. Eligible applicants must hold a contractual obligation with SLaM such as an honorary contract or research passport and, if access to ONS data is involved, must have Approved Researcher accreditation. For an application to gain approval it must:
1. Satisfactorily demonstrate underlying value and potential benefits to patient care;
2. Have appropriate supervision and governance - e.g. research governance for research projects; formal clinical governance approval for audits; SLaM director sign-off for service evaluation;;
3. Have an appropriate design to minimise inadvertent risk of inappropriate re-identification of patients - e.g. the likelihood of particularly small cohort / cell sizes (< 10 cases); appearance of high profile publically known/published information, etc. In these cases additional measures may be put in place to safeguard confidentiality;
4. Be within the scope of investigating the associations between specific mental disorders in secondary mental health care (schizophrenia, schizoaffective disorder, bipolar disorder and dementia) and physical illness;
All research projects are carried out within the SLaM NIHR Biomedical Research Centre for Mental Health and the linked data remain within the SLaM NHS firewall at all times (as is the security model requirement for all analyses of SLaM data, regardless of data linkage). Researchers using the data are (and will be) required to have a SLaM substantive or honorary contract, or a research passport.
The honorary contract and research passport encompass a HR agreement between prospective researchers and the South London and Maudsley NHS foundation trust enabling individuals to have access to the linked data for research purposes. Both the honorary contract and research passport ensure that users with access to linked data are contractually obliged to adhere to relevant SLaM Trust policies regarding confidentiality and data protection. Whether a researcher requires an honorary contract or a research passport is dependent on the work that the individual will be doing within SLaM, the attached ‘HR Good Practice Resource Pack’ and ‘Honorary Research Contracts Principles and Legal Requirements’ documents from the NIHR set out the guidelines and requirements for honorary contracts.
Approval is only sought for use of HES data incorporating the SLaM linkage (i.e. not for analysis of HES data alone). Broadly, the studies using the linkage have adopted the following designs:
1. Investigations carried out on HES data from the SLaM catchment, identifying a HES-derived outcome and comparing its occurrence between people with/without a given mental disorder in order to derive standardised morbidity ratios (for example, some current research investigating respiratory disease admissions in people with learning disability compared to the local population).
2. Investigations restricted to people with a given HES-derived outcome and comparing subsequent events between people with/without a given mental disorder (for example, further analyses of people with/without a learning disability who have a respiratory disease admission, comparing duration of hospitalisation and risk of readmission between the two groups).
3. Investigations restricted to people with a given mental disorder investigating one or more HES-derived outcomes in relation to SLaM-derived information (for example, investigating the relationship between mental health symptom profiles and physical health events in people with severe mental illness).
4. Investigations primarily carried out using SLaM data, where HES-derived information is used to provide supplementary information (for example, the ability to adjust for serious physical illness in a number of analyses). This includes the use of mental healthcare data contained on HES for residents in the SLaM catchment to capture mental health service use by providers other than SLaM (e.g. out-of-catchment hospitalisations).
5. Investigations primarily carried out using SLaM data where a HES outcome is used to define the sample (for example, a series of analyses investigating medication and health outcomes before and after childbirth in women with pre-existing severe mental illness).
The approvals also cover ONS mortality data linked to SLaM, which had been first agreed in 2005 with the Office for National Statistics. The use of these data is for analyses of mortality outcomes in people with mental disorder, either within-group (comparing different characteristics as predictors in a survival analysis) or using national data for standardisation.
These studies have focused on describing the higher mortality experienced by people with mental disorders and are now moving into the investigation of specific causes of death, as well as other relevant outcomes (e.g. place of death).
The data will only be used for purposes relating to the provision of healthcare or the promotion of health in line with the requirements of the Health and Social Care Act 2012 as amended by the Care Act 2014.
Linkages of the South London and Maudsley Case Register to mortality and healthcare data were originally set up because of the recognised substantial disparities in health experienced by people with mental disorders. Although there have been calls for a shift from observational to interventional research in this area, it has also been pointed out that there are still many aspects of the link between mental and physical health which are unclear (Stewart, 2015). To begin with, the scale of the challenge needed delineating. Data derived from our Case Register, accessed via the Clinical Record Interactive Search (CRIS) platform (Stewart et al., 2009; Perera et al., 2016a), were linked to mortality data in order to provide estimates of life years lost in different mental disorder groups: data which remain the only UK source of information on this topic (Chang et al., 2011). Having demonstrated this, a key task has been to identify the disorders responsible and sub-populations most at risk – essential for targeting interventions appropriately, but still relatively under-investigated. Recently published CRIS data linked to ONS mortality data were used to estimate the contributions of different causes of death to life expectancy loss (and hence the life expectancy gains which could potentially be accrued if these were equalised to general population norms); key findings were that a wide range of different causes of death were responsible for the life-expectancy loss, indicating that public health interventions need to focus on factors with multiple health benefits rather than single disorders (Jayatilleke et al., 2017). However, we have used the linkages to investigate and highlight specific pathways, such as suicide (Lopez-Morinigo et al., 2014; 2016), the high mortality experienced by people with substance use disorders who experience transfers of care (Bogdanowicz et al., 2015; 2016), and unexpected deaths in people receiving antipsychotic medication (Mace et al., 2015). A particular advantage of CRIS is the wealth of data provided which can be used to ascertain clinical subgroups with higher or lower risk of mortality (both overall and by cause of death). Investigations to date have included the following predictors: ethnicity (Das-Munshi et al., 2017), clinical risk assessments (Wu et al., 2012), and global clinical/functional profiles (Hayes et al., 2012a; 2012b). Weekend admissions have been evaluated in view of concerns about higher mortality in other specialties, but were not found to predict mortality in mental healthcare (Patel et al., 2016). Antipsychotic polypharmacy exposure has been recently investigated, but was not found to be a significant risk factor in most analyses (PhD thesis – G Kadra), while clozapine use was found to be associated with a markedly reduced risk of mortality (Hayes et al., 2015). Finally, mortality has been evaluated in specific clinical diagnostic groups including personality disorder (Fok et al., 2012; 2014) and chronic fatigue syndrome (Roberts et al., 2016). The CRIS linkage to Hospital Episode Statistics (HES) has been used to investigate health inequalities at the level of hospitalised disorders, including a recently published demonstration of the high risk of respiratory disease admissions in patients with learning difficulties, as well as longer durations of hospitalisation and higher risk of readmission (Chang et al., 2017). Current ongoing investigations have described the most common reasons for hospitalisation in people with severe mental illness (PhD thesis – N Jayatilleke), as well as associations with antipsychotic polypharmacy (PhD thesis – G Kadra) and with symptom profiles (PhD thesis – N Jayatilleke). Finally, HES data have been used to define childbirths, and therefore pregnancy episodes in women with severe mental illness in order to investigate health and use of medication in pregnancy (Taylor et al., 2015; 2016), extending more recently into analyses of obstetric procedures and outcomes at the time of childbirth (PhD thesis – C Taylor). Linkages with hospitalisation and mortality data have been used more specifically in dementia research, given the nature of the disorder as a condition of late-life and thus associated causally or coincidentally with a number of other age-associated comorbidities. This was a recent focus of an All Party Parliamentary Group and we used combined CRIS and HES data to quantify levels of hospitalisation prior to and after clinical diagnoses of dementia – a topic which is being developed further (PhD thesis – U Gungabissoon). Other investigations using HES data have included a study of end-of-life hospitalisation burden in dementia (Sleeman et al., 2017), and a model of costs associated with different severity levels of dementia (Knapp et al., 2016), substantially improving on current data used by NICE for dementia treatment evaluation. Investigations using linked mortality data have included analyses of the accuracy of recording of dementia on death certificates (Perera et al., 2016b), predictors of mortality in delirium (Ward et al., 2015), and analyses of cognitive function (Su et al., 2014) and antipsychotic use (Sultana et al., 2014) as specific predictors. Translation of these findings into health and social care actions is an ongoing process. Part of this has simply involved ensuring that investigations are prioritised with the health inequalities agenda in mind and then maximally disseminated. As a result our demonstration of physical health inequalities faced by people with mental disorders has been influential in shaping government mental health policy with its increasing focus on health improvement. At a local level, the South London and Maudsley Trust was the first mental health service in the UK to adopt a smoke-free policy, largely driven by the investigations using linkages with mortality and hospitalisation data. Specific issues, such as high mortality in patients misusing opiates, have also been important in shaping and targeting clinical practice towards high risk groups. We have maintained high levels of patient involvement throughout, including more recently a group set up to consider and advise on data linkages and their output specifically.
It has been established that people with most mental disorders, including neurodegenerative conditions, have substantially worse physical health outcomes (for example, 10-15 years lower life expectancy in analyses of SLaM data, with much of this life expectancy loss accounted for by mortality associated with physical disorders). However, relatively little is known about the health conditions underlying these inequalities, although this knowledge is clearly important in order to develop interventions to improve the situation.
The over-arching objective of this research programme is to provide information that will assist in narrowing the mortality and physical morbidity disadvantage experienced by people with mental disorders. Improvement in the physical health of people with mental disorders is highlighted regularly in Government policy (e.g. ‘Closing the gap: priorities for essential change in mental health’, 2014) and the monitoring of physical health outcomes is increasingly becoming a metric for mental health Trusts, as well as for national structures such as the PHE Mental Health Intelligence Network. The SLaM-KCL collaboration using CRIS and associated linked data has led the field in informing and influencing such policy, for example generating what are to date the only UK data on life expectancy in mental disorder (PLoS One 2011;6:e19590) and providing the Department of Health with data on premature mortality rates in mental disorders (J Campion – personal communication).
At a local level, these findings have also been more specifically influential in driving the implementation of a smoke-free policy across SLaM estates. As the largest centre for mental health research in Europe, SLaM are well-placed to ensure that findings from this project are effectively disseminated and will be nationally influential.
The research SLaM enables will provide novel and important information to inform these policy initiatives. Physical health disadvantages are likely to cross multiple disorders and multiple levels of morbidity: from mortality to non-fatal conditions, and from the individual impact of serious health conditions to the wider economic impacts of increased secondary care use, longer hospitalisations, and increased risk of readmission. There is therefore a need for a coordinated series of analyses to inform on specific areas of inequality in order to target interventions to improve health.
In order to improve morbidity and mortality through health and social care interventions, it is important both to have information on the adverse outcomes potentially underlying disadvantages and to be able to characterise groups most at risk of these outcomes.
The HES and mortality outcomes will address the first information need, through the proposed analyses investigating the most common reasons for acute hospitalisation in people with mental disorders, and their relative risk in relation to the local population – not only for the hospitalisation itself, but also for adverse outcomes following hospitalisation such as longer duration of hospitalisation, lower intervention receipt (where intervention coding is available – e.g. for surgical procedures), and higher risk of readmissions with the physical condition in question or a recognised complication, as well as mortality for different causes of death.
CRIS (mental healthcare) data will in turn be used to address the second information need – i.e. allowing the definition of mental health characteristics of people most at risk of adverse physical health outcomes. For example, SLaM have already demonstrated that all-cause mortality is more strongly predicted by functional impairment than general symptom severity in severe mental illness (Journal of Psychosomatic Research 2012;72:114-9, PLoS One 2012;7:e44613) and more by clinician-appraised risk of self-neglect than by appraised risk of suicide or violence (Psychological Medicine 2012;42:1581-90). This is important because mental healthcare priorities (on symptom improvement and risk of suicide/violence) have therefore not been optimally focused for mortality prevention and there has consequently been a shift in emphasis towards wider health promotion.
In relation to clarifying populations at risk, rapid advances in text-mining and their implementation in CRIS now allow detailed information to be gathered for analyses not only on mental disorder diagnoses, demographic factors and service contacts (i.e. what might be available administrative data in any other Mental Health Trust), but also on pharmacotherapeutic and psychotherapeutic interventions, detailed symptom profiles, risk lifestyles (e.g. smoking, illicit drug use), and adverse drug events. For example, ‘negative’ symptoms of schizophrenia have been ascertained through text-mining, have been demonstrated to predict worse mental healthcare outcomes (BMJ Open 2015;5:e007619) and are currently being investigated along with 45 psychotic and 15 depressive symptoms as predictors of adverse physical health outcomes, including mortality. Symptom profiles are recognised to be important predictors of psychosis outcomes and are the primary focus for mental healthcare interventions; however, they are ‘invisible’ in routine healthcare data because they are recorded in text rather than structured fields. The proposed analyses against HES and mortality outcomes are thus only possible at the moment (internationally as well as in the UK) in the CRIS resource at SLaM – hence they are uniquely positioned to provide influential investigations of physical health outcomes in mental disorders at a level of detail which would not be possible through any other route.
The information on physical health outcomes from HES and mortality will allow policies and interventions to be developed and evaluated within acute and primary care services to serve better people with mental disorders, while information from the mental health record is particularly relevant to mental health services in clarifying patient groups who are most vulnerable. Dissemination targets are therefore broad and cut across primary, acute and mental health care sectors, as well as involving multiple levels from clinicians and Trusts delivering care to commissioners and NHS structures overseeing this, as well as bodies such as PHE with broader health improvement oversight. Furthermore, audiences differ by age-ranges and disorders of interest (e.g. those interested in physical disorders in young adults with severe mental illness may be different from those interested in the acute care impact of dementia in later life). Therefore, in addition to the proposed programme of academic publication and dissemination, SLaM will prepare a 3-yearly report summarising findings over the previous 3 years and their implications.
The primary output of the linkage is the production and maintenance of a research resource for the purpose of use in informative research analyses for publication in peer-reviewed journals and other standard routes of academic dissemination (e.g. conference presentations).
All secondary outputs (whether tables or visuals) will only include aggregated data and will not include any description of a cell size below 10.
In terms of target dates, SLaM expects that a minimum of five research papers would be published per year using the proposed data linkages. Examples of papers recently published or planned for publication include:
1. A study investigating respiratory disease admissions in people with learning disabilities receiving SLaM services. This has shown clear disadvantage in terms of risk of readmission and length of stay, and is important for service planning for learning difficulties populations. Currently submitted for publication.
2. A current BRC PhD studentship using CRIS to investigate medication and medication changes during pregnancy in women with severe mental disorders (e.g. schizophrenia and bipolar disorder), and mental health outcomes before and after childbirth. This is one of the world’s largest investigations of this question and highly relevant for services caring for women with severe mental disorders and the treatment decisions required around pregnancy. Two papers have been published (Taylor et al. BMC Psychiatry 2015; 15: 88. Taylor et al. Archives of Women’s Mental Health 2016 May 13. [Epub ahead of print]) and a further three have been submitted or are in preparation.
3. A study of care home and hospitalisation costs associated with levels of cognitive function in people with dementia, carried out in order to inform NICE decisions about cost-benefits of dementia treatments – the largest and most generalisable study of this issue to date. Currently submitted for publication.
4. A current BRC PhD studentship study of medication profiles in people with severe mental disorders and physical health outcomes associated with these. The focus so far has been on antipsychotic polypharmacy – an important issue in routine clinical care but one for which there has been little or no evidence base to date. Two papers have been published (Kadra et al. BMC Psychiatry 2015; 15: 166. Kadra et al. Schizophrenia Bulletin 2016 Apr 15. [Epub ahead of print]) and a further two papers are in preparation. In addition, recent findings of a protective association of clozapine with mortality, despite multiple adjustments and applying both to natural and external-cause deaths, has been influential and very highly cited (Hayes et al. Schizophrenia Bulletin 2015; 41: 644-655).
5. A current BRC PhD studentship investigating symptom profiles in people with severe mental disorders and their associations with cardiovascular disease admissions. Three papers are currently in submission or preparation, the aim being to investigate the profiles of people with mental disorders who are most at risk of adverse physical health outcomes.
6. A study describing acute sector hospitalisations in people with eating disorder diagnoses. Analyses have been completed, showing substantial increased risk of a range of adverse physical health outcomes and the paper is currently in preparation.
7. A study of describing stroke incidence and its predictors in people with dementia. The paper is currently in submission.
8. Several studies investigating predictors of suicide as a specific cause of death. The E-Host-IT study (funded by an Academy of Medical Sciences Fellowship) investigates fine-grain text predictors of suicide risk. The Pheme consortium (funded by EU FP7) seeks to investigate suicide risk temporally associated with phenomena on social media. In addition a Mental Health Research UK funded PhD studentship is investigating antidepressant profiles in relation to suicide and suicide-related behaviour.
9. The range of publications to date on mental disorders and mortality risk have been influential in shaping national mental health policy (our findings on reduced life expectancy remain the only UK data on this to date), promoting physical healthcare in these populations, as well as on local policy (e.g. SLaM has been the first mental healthcare provider to adopt a smoke-free policy in all its units).
10. Data on general hospital use before and after a dementia diagnosis, using the CRIS-HES linkage, has been cited in a recent All-Party Parliamentary Group report on comorbidity in dementia.
For a full list of CRIS publications including those that have used linked data please see the Maudsley BRC website at the following address: http://www.maudsleybrc.nihr.ac.uk/about-us/core-facilities/clinical-record-interactive-search-cris/cris-publications/.
The utility of the linkages with HES and mortality data is to allow investigations of physical health outcomes in people with mental disorders, as the linked databases primarily contribute this information. Most of the work using the linked information is best classified as ‘research’. However, where an investigation is primarily evaluating these outcomes in a single service with a view to evaluating its performance with a view to improving this, it is likely to be more appropriately categorised as clinical audit. Examples of previous clinical audit projects which have used such linked data include: i) surgical outcomes in people with severe mental illness; ii) deaths occurring in patients receiving care from Trust Addictions services; iii) physical healthcare of substance misusing patients in Lambeth. Linked data have also been used to supplement outcomes in studies best categorised as ‘service development/evaluation’, for example a monitoring and evaluation workstream of an adult mental health programme, and of a home treatment team intervention in Croydon. These studies do not differ in their nature from research studies using the linked data; it is simply that their focus is on specific service evaluation and the relevance of their findings is generally for the Trust rather than the research community. Linked data are primarily of use for baseline or single-stage audits, because SLaM do not have a ‘live’ data feed from these linkages for real-time evaluation of interventions or repeat audits.
All the potential uses of the linked data fall within the stated primary purpose of investigating physical health in people with mental disorders. The only additional request is to be permitted to use the linked data on occasions to ascertain mental healthcare received outside the SLaM catchment. This is particularly important in longitudinal studies of SLaM’s patient cohorts where admissions for relapses in mental disorders of interest may occur in different inpatient units (particularly within London) and is therefore an example of the use of linked data outside the physical health outcome category.
The data being requested will only be used for the purpose described. Any proposed changes will be submitted to the HSCIC for approval before implementation.
Publication targets will clearly depend on the nature of individual findings and the potential audience envisaged. Where possible, SLaM will target general medical and/or public health journals with a broad audience, because analyses are likely to cross disciplines; however, they will also consider specialist journals within the mental health field as well as the individual medical specialties implicated. Dissemination at national and international conferences will adopt a similar strategy of aiming for as broad as possible a reach. They will include mental health focused meetings such as the Royal College of Psychiatrists and European Psychiatric Association congresses, and psychiatric epidemiology meetings such as the International Federation of Psychiatric Epidemiology (IFPE) but SLaM will also seek presentations at medical specialty conferences where results have relevance to those audiences, as well as meetings where commissioners are likely to be represented.
For each application received, the CRIS Oversight Committee, considers the study design and advises on optimisation of benefits. The CRIS Oversight Committee also has a responsibility for publicity and dissemination of findings to relevant parties, media and patient groups.
For example, a major programme of work has involved describing the physical health needs and inequalities faced by people with severe mental disorders. As well as featuring in regular patient/public focused dissemination events on CRIS findings, periodic blogs and editorial pieces have been written summarising the CRIS-derived evidence as it emerges, so that an up-to-date picture is maintained. This work has also been influential at a national level in shaping government policy on physical health needs in mental healthcare and several policy-focused reports have been prepared.
In addition to these traditional routes of academic dissemination, SLaM will seek internal funding to support the production and dissemination of a 3-yearly report of all work on this project, summarising the key findings and their health/social care implications. The report will be made available online with appropriate supporting resources, and will have an executive summary of key points raised.
Excluding patients that opted out of participation, SLaM extracts patient identifiers for all registered users of SLaM’s facilities, SLaM extracts patient identifiers from SLaM’s electronic patient record, as well as the CRIS pseudonym (known as the BRCID) from the CRIS system. The identifiers are securely transferred to the HSCIC and the HSCIC returns HES and ONS Mortality data linked to the CRIS BRCID with all patient identifiers removed other than Date of Death. Additionally the HSCIC provides a bespoke HES extract of all residents in SLaM’s geographic catchment (Lambeth, Southwark, Lewisham and Croydon boroughs). All supplied HES and Mortality data are held separately by the SLaM Clinical Data Linkage Service (CDLS) and are only accessible to a restricted number of approved technical support staff.
Researchers do not have access to the raw HES or ONS data.
The HES and ONS data will not be linked with patient identifiers from SLaM’s electronic patient record and no attempt will be made to identify individuals in the data under any circumstances.
When an application has been approved by the CRIS Oversight Committee, technical staff – all of whom are substantive employees of SLaM – assemble bespoke de-identified linked databases meeting the approved requirements of the research study. These are deposited in shared network drives within the SLaM network. Approved researchers can only access the data on location within the SLaM network. All research databases remain within the SLaM firewall at all times on the SLaM network. A dedicated office suite, the BRC Nucleus, has been set up at the SLaM Biomedical Research Centre in order to facilitate analyses using SLaM data. Removal of data from this environment is expressly forbidden other than in the form of aggregated summary data with small numbers suppressed in line with the HES Analysis Guide.
For each research database created a different encoded identifier variable (anonym) is assigned meaning there are no common identifiers or pseudo-IDs across different databases making it impossible for researchers to link their database with source SLaM or HES data. This uses a one-way encryption method following which anonyms cannot be reverse engineered.
At the completion of research projects, the databases used are removed from the shared network drive and archived for a period of 10 years and then permanently destroyed.